Metagenomics Reveals Pathogenic Diversity and Temporal Dynamics in Severe Pneumonia Among Patients in Adult Intensive Care Unit

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In this study, we harnessed the power of mNGS to explore the pathogenic spectrum and temporal variations in respiratory tract specimens collected from adult patients with severe pneumonia in the Intensive Care Unit (ICU) of a hospital in Guangxi. Methods From December 2021 to July 2022, 44 respiratory tract samples (including sputum and bronchoalveolar lavage fluid ) from 25 adult patients(comprising 18 males and 7 females) diagnosed with severe pneumonia and admitted to the ICUs of two hospitals in Guangxi. A customized mNGS detection protocol was developed and applied for analyzing the composition and temporal variations of pathogens within the respiratory tract samples. Results Among these patients, the bacteria, fungi, and viruses were markedly higher detected by mNGS compared to conventional microbial culture methods ( P < 0.001). The most prevalent bacteria detected were Stenotrophomonas maltophilia (61.36%), Corynebacterium striatum (54.55%), and Escherichia coli (54.55%). The viruses with the highest detection rates were human herpesviruses(HSV-1, 31.82%;HCMV, 27.27%;HSV-2, 11.36%). The most frequently identified fungi were Candida albicans (50%) and Candida glabrata (27.27%). Single-pathogen infections accounted for 64% (28/44) of the cases, while mixed-pathogen infections comprised 36% (16/44). Dynamic monitoring using mNGS in 8 patients uncovered diverse respiratory pathogenic spectra, with the majority of patients exhibiting dynamic changes that correlated with fluctuations in inflammatory markers such as leukocyte counts, procalcitonin levels, and C-reactive protein levels, alongside the clinical progression of the disease. Conclusion mNGS exhibits superior performance in diagnosing mixed infections and real-time tracking of the pathogen spectrum, which provide a robust empirical basis for guiding clinical diagnosis and treatment strategies of patients in ICU. Metagenomic next-generation sequencing (mNGS) Intensive Care Unit(ICU) Severe pneumonia Respiratory tract Dynamic monitoring Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Respiratory tract infections have consistently been a major focus in global public health, posing enduring challenges [ 1 ]. Lower respiratory tract infections (LRTIs) contribute significantly to the global disease burden, ranking as the second leading cause [ 2 ]. LRTIs encompass various conditions, including community-acquired pneumonia (CAP), hospital-acquired pneumonia (HAP), ventilator-associated pneumonia (VAP), acute bronchitis, bronchiolitis, and bronchiectasis [ 3 ]. Severe lung infections are a primary cause of mortality from infectious diseases [ 4 , 5 ]. Severe pneumonia is typically caused by a diverse array of pathogens, including bacteria, viruses, and fungi [ 6 ]. Given its rapid onset, swift progression, complex etiology, and wide range of pathogens, timely identification of the causative pathogens is crucial for effective clinical intervention [ 7 , 8 ]. Traditional pathogen detection methods, such as smear analysis, histopathological examination, culture, serum antibody tests, and polymerase chain reaction (PCR), have limitations in terms of timeliness, specificity, and sensitivity, falling short of modern diagnostic requirements [ 9 – 11 ]. While PCR offers rapid and specific detection of predefined pathogens, mNGS provides an unbiased approach capable of detecting a wider range of pathogens, including novel or unexpected taxa. In recent years, metagenomic next-generation sequencing (mNGS) has risen to prominence as a powerful tool in the diagnosis and treatment of infectious diseases, thanks to its efficient and broad-spectrum pathogen identification capabilities [ 12 ]. The unique advantage of mNGS is its untargeted nature, allowing the detection of pathogens without prior knowledge, solely through high-throughput sequencing of clinical samples [ 13 ]. This method enables comprehensive and relatively timely pathogen detection, making it particularly suitable for acute, critical, and complex clinical scenarios [ 14 , 15 ]. Despite significant advancements in clinical pathogen detection, there is still a lack of research on the dynamic changes in pathogen spectra in severe lower respiratory tract infections using mNGS. Understanding the dynamic evolution of pathogen spectra in adult ICU patients with severe pneumonia is crucial for treatment planning and adjustment. Therefore, we utilized mNGS to investigate the dynamic changes in pathogen spectra across a series of samples from patients with severe, persistent respiratory tract infections. Additionally, we analyzed the correlation between pathogenic microorganisms and patient outcomes, aiming to provide data-driven support for optimizing clinical treatment strategies. MATERIALS AND METHODS Study Design and Participants This study enrolled 25 adult patients with severe pneumonia who were admitted to the Respiratory Intensive Care Unit (ICU) of the First Affiliated Hospital of Guangxi Medical University and the Fourth People's Hospital of Nanning between December 2021 and July 2022. All patients satisfied the diagnostic criteria for severe pneumonia established by the Chinese Society of Respiratory Diseases and the American Thoracic Society. Individuals with incomplete medical histories, HIV, or tuberculosis were excluded from the study. Bronchoalveolar lavage fluid (BALF) or sputum samples were collected from all 25 patients with severe pneumonia. These samples were concurrently subjected to traditional clinical microbiology tests, multiplex PCR, and metagenomic next-generation sequencing (mNGS). Eight of these patients underwent serial sampling at multiple time points (every three days), resulting in the collection of 3 BALF and 24 sputum samples. Furthermore, 17 patients were sampled at a single time point, providing a total of 17 additional sputum samples. Detailed demographic information, sample types, and clinical characteristics for each patient are summarized in Supplementary Table S1. The study was approved by both the hospitals' ethics committees and the Chinese Center for Disease Control and Prevention Scientific Research Ethics Review Committee. Informed consent was obtained from each patient or their authorized representative prior to participation. Clinical Data Collection and Measurement of Inflammatory Markers Clinical information was gathered from electronic medical records and encompassed a range of details including demographic characteristics, medical history, dates of onset and sampling, primary clinical manifestations, imaging findings, clinical diagnoses, results from traditional culture tests, blood laboratory test results obtained at the time of metagenomic next-generation sequencing (mNGS) sampling, and details of antimicrobial usage. All clinical data are compiled in Supplementary Table S1. White blood cell count (WBC), C-reactive protein (CRP), and procalcitonin (PCT) levels were measured from peripheral blood samples collected contemporaneously with respiratory samples, using standard automated clinical analyzers in the hospital's central laboratory. Multiplex PCR Detection A 500 μl sample was collected and subjected to DNA/RNA extraction using the QIAamp cador Pathogen Mini Kit (Qiagen, USA). Subsequently, primary pathogen screening was conducted utilizing the respiratory pathogen multiplex detection kit (Shanghai Jienuo Biotechnology Co., Ltd.). This kit utilizes reverse transcription to generate cDNA from RNA, followed by multiplex PCR for the qualitative detection of 22 respiratory pathogens, encompassing 16 RNA viruses (including influenza A virus, influenza B virus, influenza A virus H1N1, respiratory syncytial virus A and B, parainfluenza virus -1/-2/-3/-4, coronavirus OC43, coronavirus NL63/HKU1, coronavirus 229E, rhinovirus, enterovirus, human metapneumovirus), 2 DNA viruses (adenovirus and bocavirus), and 4 bacterial species ( Chlamydia pneumoniae , Mycoplasma pneumoniae , Legionella pneumophila , Bordetella pertussis ). The sample underwent an initial real-time PCR reaction on a PCR machine, following this specific protocol: 50°C for 10 minutes, 95°C for 2 minutes, followed by 40 cycles of 94°C for 20 seconds, 55°C for 20 seconds, and 72°C for 35 seconds. Subsequently, the reaction was transferred to the LightCycler480 II real-time PCR instrument, with the following parameters: 95°C for 2 minutes, followed by 10 cycles of 94°C for 15 seconds, 55°C for 15 seconds, and 72°C for 15 seconds; then 23 cycles of 94°C for 15 seconds, 50°C for 15 seconds, and 72°C for 15 seconds. The program concluded with a step at 95°C for 2 minutes, followed by a 90-second melting curve analysis ranging from 40°C to 90°C, with fluorescence detection occurring at a rate of 1°C/s. Finally, a 1-second cooling period at 37°C was included. Metagenomic Sequencing and Data Analysis After following standard procedures for collecting sputum or BALF samples, viscous samples were liquefied using sputum digest at a 1:1 ratio (Sputasol method) and incubated at 37°C for 30 minutes to obtain a homogeneous solution. Subsequently, 500 μl of the sample was used for nucleic acid extraction with the QIAamp DNA microbiome kit (Qiagen, USA). Human host nucleic acids were removed using Benzonase (Qiagen, USA). The concentration of DNA was determined using Qubit dsDNA HS Assay Kits (Thermo Fisher, USA) on a Qubit 3.0 fluorometer (ABI, USA). The DNA was then diluted to 0.2 ng/μl, and library construction was performed according to the instructions provided in the Nextera XT DNA Library Preparation Kit (Illumina, USA). Finally, the prepared libraries were sent to Tianjin Nuohe Zhiyuan Bioinformatics Technology Co., Ltd., where they underwent metagenomic sequencing using the Novaseq-PE150 sequencer (Illumina, USA). To ensure the absence of contamination, a template-free control (DNase/RNase-free water) was included alongside the respiratory samples for quality control purposes during the mNGS process. Raw sequencing reads were processed using a standardized bioinformatics pipeline. First, low-quality reads, adapter sequences, and short reads (<50 bp) were filtered using Fastp (v0.23.2). Human-derived reads were then identified and removed by alignment to the GRCh38 reference genome using BWA-MEM (v0.7.17). The remaining high-quality, non-human reads were classified for microbial taxonomy using Kraken2 (v2.1.2) with the Standard database, and abundance was estimated with Bracken (v2.6.0). Potential pathogens were identified based on a reads per million (RPM) threshold of >10, after subtraction of any reads also detected in no-template controls (NTCs), following the interpretive framework suggested in the mNGS quality control expert consensus.. Statistical Analysis All statistical analyses were performed using SPSS 22.0 (SPSS Inc., Chicago, IL, USA), GraphPad Prism 8 (GraphPad Software, Inc., La Jolla, CA, USA), and R 4.0 software. Continuous variables that adhered to a normal distribution were reported as the mean ± standard deviation, whereas those with skewed distributions were described by the median and interquartile range. Categorical variables were presented as frequencies and percentages. Comparisons of continuous variables were conducted using either the t-test or the Mann-Whitney U test, depending on the data distribution. Categorical variables were compared using Pearson's chi-square test or Fisher's exact probability method, as appropriate. Statistical significance was determined based on a two-sided test with a P < 0.05. RESULTS Demographic and Clinical Characteristics of Severe Pneumonia Cases Based on the inclusion criteria, this study encompassed 25 cases of severe pneumonia, comprising 18 males (72%) and 7 females (28%). Laboratory analyses revealed a white blood cell count of 12.05 (9.82, 17.81) × 10^9/L, with a neutrophil percentage of 87.00 (78.44, 92.66)% and a lymphocyte percentage of 5.26 (3.84, 10.59)%. The procalcitonin level was 2.10 (0.62, 5.20) ng/mL, and the C-reactive protein level was 117.3 ± 54.84 mg/L. Among the patients, 8 (32%) had fever, and the majority had underlying medical conditions: 14 (56%) had hypertension and 5 (20%) had diabetes mellitus. Furthermore, 13 (52%) patients developed sepsis/septic shock, 20 (80%) had a history of mechanical ventilation, and 8 (32%) had a smoking history. The length of hospital stay for 13 (52%) patients ranged from 10 to 30 days. Overall, 20 (80%) patients survived, while 5 (20%) died. The mean age of discharged patients was 66.90 ± 16.33 years, compared to 65.60 ± 11.06 years for deceased patients. Significant differences were observed in platelet count and serum procalcitonin levels between the discharged and deceased patients (P < 0.05). Additionally, the discharged patients had a significantly longer hospital stay compared to the deceased patients (P < 0.01). These findings are summarized in Table 1. Preliminary Screening and Rationale for Metagenomic Sequencing All collected respiratory samples were initially screened using the respiratory pathogen detection kit as described above. Notably, all samples tested negative for the 22 targeted respiratory pathogens (including 18 viruses and 4 bacteria). Given that the clinical presentation and history of the enrolled severe pneumonia patients were not typical for common viral respiratory infections, and considering the negative results from this broad targeted panel, we hypothesized that the causative agents might be outside its detection scope. To enable a comprehensive and hypothesis-free investigation, we proceeded to perform mNGS on all samples. Comparison of mNGS Results with Traditional Culture Assays We initially evaluated the diagnostic efficacy of mNGS and traditional culture methods in identifying pathogens, as depicted in Figure 1. Our study involved samples from 25 patients, from which a total of 53 pathogens were identified using both methodologies. These pathogens included 32 bacteria, 10 fungi, and 11 viruses. Overall, mNGS exhibited a wider range of microorganism detection compared to traditional culture techniques. The most commonly detected bacteria were Stenotrophomonas maltophilia, Corynebacterium striatum, Escherichia coli, Acinetobacter baumannii, and Klebsiella pneumoniae. Among the fungi, Candida albicans, Candida glabrata, Candida dubliniensis, and Candida orthopsilosis were frequently identified. Notably, mNGS demonstrated significantly higher detection rates for pathogens such as Pseudomonas aeruginosa, Klebsiella pneumoniae, Acinetobacter baumannii, Burkholderia cepacia, Candida glabrata, Candida albicans, and Aspergillus fumigatus compared to traditional culture methods (P < 0.001), as illustrated in Figure 1A and Figure 1B. Additionally, mNGS predominantly identified viruses such as Human alphaherpesvirus 1, Human betaherpesvirus 5, Human alphaherpesvirus 2, and Human gammaherpesvirus 4. Pathogen Profiling of Respiratory Tract Samples Upon further analysis of the lower respiratory tract samples, we observed the presence of dominant species with relative abundances exceeding 30% in specific samples (see Figure 2). It should be noted that relative abundance is dependent on the sequencing depth and community composition; all samples met the minimum sequencing data requirement recommended for BALF/sputum analysis (>20 million reads). Notable examples include Candida orthopsilosis in sample N1_2B, Candida albicans in samples N5-1S, N6-1S, and N17-1S, Candida glabrata in sample N17-1S, Aspergillus fumigatus in sample N25-1S, Klebsiella pneumoniae in samples N5-2S, Corynebacterium striatum in samples N3-1S, N4-1S, and N11-1S, Acinetobacter baumannii in samples N1_1B, N1_3B, and N19-1S, Staphylococcus aureus in sample N10-1S, Escherichia coli in sample N2-4S, Stenotrophomonas maltophilia in samples N2-2S, N2-4S, and N18-1S, as well as in samples N3-2S and N5-3S. Pseudomonas aeruginosa was detected in sample N12-1S, while Burkholderia cenocepacia was found in samples N2-2S, N2-3S, and N9-1S. To preliminarily explore associations between clinical status and microbial profiles, patients were stratified by clinical outcome (survivors vs. non-survivors) and length of ICU stay ( 0.05), likely due to small sample size, visual inspection suggested trends (e.g., higher relative abundance of Acinetobacter baumannii in non-survivors). These observations warrant validation in larger cohorts. These findings suggest a potential association between the patient's clinical condition and the abundance of microorganisms in the sample. Notably, there was a prevalence of nosocomial infections among the dominant species, such as Klebsiella pneumoniae , Acinetobacter baumannii , Escherichia coli , and Pseudomonas aeruginosa , indicating that patients with prolonged antibiotic use or extended hospital stays may be more susceptible to these infections. In our study, a total of 44 lower respiratory tract samples were collected. Of these, 64% (28/44) were single-pathogen infections, while 36% (16/44) were mixed-pathogen infections. The mixed-pathogen infections comprised 13 cases of bacterial and fungal infections and 3 cases of bacterial and viral infections. It is important to note that our cohort primarily consisted of sputum samples (n=41), with only 3 BALF samples. While sputum may be subject to upper respiratory tract contamination, it remains a commonly used specimen in severe pneumonia. The interpretive challenges of differentiating colonization from infection in sputum are acknowledged, and findings should be considered in this context. Analysis of Dynamic Changes in Pathogen Spectrum In this study, we conducted serial metagenomic next-generation sequencing (mNGS) testing on eight cases of severe pneumonia to monitor the dynamic changes in pathogens throughout the disease course. Simultaneously, we analyzed variations in inflammatory markers, including leukocytes, procalcitonin, and C-reactive protein, to investigate the relationship between the clinical progression of severe pneumonia and shifts in the respiratory pathogen microbial profile (see Figure 3). In samples from Cases 1 and 5, the sequential alterations in fungal relative abundance paralleled the changes in white blood cell count and C-reactive protein, both showing a consistent decline. After the administration of effective anti-infective therapy, the detection of fungal sequences by mNGS diminished within two weeks and ultimately became undetectable. Similarly, leukocyte and procalcitonin levels also demonstrated a decreasing trend. In contrast, bacterial diversity and relative abundance gradually increased, suggesting an heightened risk of nosocomial infection with extended hospital stays (refer to Figures 3A and 3E for further details). In samples from Case 2, the persistent rise in bacterial species abundance and inflammatory markers indicated ineffective anti-infective therapy (refer to Figure 3B for further details). For Case 8, the abundance of fungal species continued to decrease in concordance with changes in C-reactive protein, while the abundance of bacterial species initially decreased and subsequently increased, mirroring the trend observed in leukocytes (refer to Figure 3H for further details). In the eight severe pneumonia cases where serial sampling was conducted, the microbial profiles displayed diverse trends (Fig. 3). Specifically, species diversity declined in Cases 1 and 3, while it rose subsequently in Cases 2 and 5. Case 6 exhibited an upward trend, and Case 8 demonstrated a fluctuating pattern of increase and decrease, as illustrated in Figure 4A. Interestingly, the Shannon diversity index did not show a consistent decline during hospitalization despite antibiotic exposure. The analysis of dynamic pathogen detection results across these eight cases identified Candida albicans as the most consistently detected fungus, followed by Candida glabrata , Corynebacterium striatum , Acinetobacter baumannii , Escherichia coli , Stenotrophomonas maltophilia , and Pseudomonas aeruginosa . Additionally, Human alphaherpesvirus 1 and Human betaherpesvirus 5 were the most prevalent viruses detected, as depicted in Figure 4B. These findings reveal varying levels of species diversity and relative abundance in consecutive samples, which may be correlated with disease progression. Notably, patients with severe pneumonia exhibited a predominance of bacterial species such as Candida orthopsilosis , Candida albicans , Candida glabrata , Corynebacterium striatum , Klebsiella pneumoniae , Acinetobacter baumannii , Pseudomonas aeruginosa , and Human alphaherpesvirus 1. The majority of these species are associated with colonization or hospital-acquired infections, indicating that patients with prolonged antibiotic use or hospital stays may be particularly susceptible to nosocomial infections. DISCUSSION In this study, we utilized mNGS to analyze the pathogenic spectrum in patients with severe pneumonia, revealing dynamic fluctuations in pathogens. Initially, we compared the results of mNGS with traditional pathogen cultures while examining demographic characteristics, laboratory findings, underlying medical conditions, complications, and lengths of hospital stay among the severe pneumonia cases. Consistent with previous studies, mNGS demonstrated enhanced sensitivity and accuracy in pathogen identification compared to traditional culture methods, offering a broader range of pathogen detection and improved capabilities for detecting co-infections [ 16 – 18 ]. Pan et al.'s research identified five cases of bacterial, fungal, or viral co-infections through mNGS, allowing clinicians to conduct more comprehensive patient assessments and implement effective treatment strategies [ 19 ]. Similarly, Peng et al.'s findings indicated a high prevalence of polymicrobial infections among immunocompromised patients with suspected pneumonia, with mNGS demonstrating comparable overall diagnostic performance to conventional microbial testing as a primary diagnostic tool [ 20 ]. Therefore, in cases involving rare pathogens or limitations in routine microbial testing, a combined approach utilizing both mNGS and conventional microbial assays may constitute the optimal diagnostic strategy in clinical practice [ 21 ]. Secondly, we analyzed the pathogenic spectrum in lower respiratory tract samples collected from 44 ICU patients diagnosed with severe pneumonia. Our findings revealed that the three most prevalent bacteria were Stenotrophomonas maltophilia (61.36%), Corynebacterium striatum (54.55%), and Escherichia coli (54.55%). Among the identified pathogenic bacteria, Gram-positive species included Corynebacterium striatum , Staphylococcus epidermidis , Staphylococcus aureus , Streptococcus pneumoniae , and Enterococcus faecium , while Gram-negative bacteria comprised Escherichia coli , Klebsiella pneumoniae , Stenotrophomonas maltophilia , Pseudomonas aeruginosa , and Acinetobacter baumannii . These results are consistent with those reported by Chien et al. [ 22 ]. (A complete list of detected taxa is provided in Supplementary Table S2 ). Furthermore, our study observed an increase in the detection of pathogens, such as Acinetobacter and Corynebacterium , with prolonged hospital stays and continued antibiotic administration. Notably, certain pathogens emerged as dominant, including Corynebacterium striatum , Acinetobacter baumannii , and Klebsiella pneumoniae . This highlights the susceptibility of our patients to nosocomial infections due to extended antibiotic usage and prolonged hospital stays [ 23 ]. In recent years, Corynebacterium striatum has emerged as a multidrug-resistant pathogen capable of causing nosocomial outbreaks [ 24 , 25 ]. Klebsiella pneumoniae , a common opportunistic pathogen, is a significant contributor to nosocomial infections and exhibits a notable level of multi-drug resistance [ 26 ]. Acinetobacter baumannii has gained prominence as a key pathogen causing various infections, including pneumonia, bloodstream infections, skin and soft tissue infections, and urinary tract infections, leading to increased morbidity and mortality rates [ 27 ]. In immunocompromised patients, Acinetobacter baumannii poses a significantly higher pathogenic risk [ 28 , 29 ]. Globally, 45% of Acinetobacter baumannii isolates are classified as multi-drug resistant pathogens (MDRs) [ 30 ]. An important limitation of our study is the absence of resistome analysis. The mNGS data generated here hold potential for profiling antibiotic resistance genes (ARGs), which, when correlated with antimicrobial treatment records, could provide direct insights into resistance selection pressure and treatment efficacy. The Chinese mNGS expert consensus outlines principles for reporting detected resistance genes alongside phenotypic correlation. Future analysis of these datasets for ARGs and their genomic context is warranted and would significantly enhance the clinical utility of mNGS in guiding antimicrobial stewardship. Among the viruses identified in this study, the three most prevalent were Human alphaherpesvirus 1 (31.82%), Human gammaherpesvirus 4 (27.27%), and Human alphaherpesvirus 2 (11.36%). These viruses primarily belong to the Human Herpesvirus genus, with Human alphaherpesvirus 1, Human alphaherpesvirus 2, Human gammaherpesvirus 4, and Human betaherpesvirus 5 being the most frequently encountered variants. In this study, Candida albicans (accounting for 50%) and Candida glabrata (27.27%) emerged as the most prevalent fungal species detected. The rise of fungal pathogen infections poses a growing concern for global public health [ 31 ]. In February 2023, the World Health Organization unveiled its first-ever list of priority fungal pathogens, emphasizing the significance of Cryptococcus neoformans , various Candida species, Aspergillus fumigatus , and Candida albicans [ 32 ]. Over the past few decades, there has been a notable increase in fungal lung infections among immunocompromised patients [ 33 ]. Our study cohort primarily consisted of elderly individuals with pre-existing medical conditions, indicating compromised immune systems and heightened vulnerability to opportunistic lung pathogens. A survey of oral fungal communities in healthy individuals revealed that 36.1% of fungal species were undetectable using conventional culture methods [ 34 ]. In contrast to the time-consuming and challenging nature of fungal cultures, mNGS has emerged as an unbiased, efficient, and rapid diagnostic tool for pathogen identification, effectively overcoming current diagnostic limitations [ 35 ]. According to Miao et al., mNGS demonstrated superior performance in fungal detection compared to traditional methods, with an odds ratio of 4.0 (95% CI, 1.6–10.3; P < 0.01) [ 10 ]. Our study also identified a variety of fungi, with Candida species, particularly Candida albicans and Candida glabrata , being the most common. Candida infections are highly prevalent in immunocompromised patients and are associated with a high fatality rate ranging from 40–60% [ 36 ]. Additionally, Aspergillus fumigatus, a common pathogenic fungus, was also detected [ 37 ]. Invasive pulmonary aspergillosis remains a major cause of death among immunocompromised patients [ 38 ]. Notably, our serial monitoring data showed a significant reduction in fungal abundance among patients receiving antifungal treatment, correlating with laboratory improvements. This underscores the crucial role of clinicians in actively targeting pathogens during therapy. In conclusion, mNGS holds considerable clinical value in the precise diagnosis of pulmonary fungal infections. In the study, it was found that 40.3% of lung fungal infection cases involved mixed fungal and bacterial infections [ 39 ]. Specifically, 36% (16 out of 44) of these mixed-pathogen infections were predominantly bacterial. A weakened immune system emerged as a significant risk factor for bacterial-fungal co-infections, and the presence of inflammatory lung lesions further increased susceptibility to such infections [ 40 ]. Additionally, the administration of multiple antibiotics suggested the presence of bacterial or more complex infections. Prolonged antibiotic use, defined as use for 14 days or more, not only promotes drug resistance but also disrupts the body's normal microbial flora, leading to microbial dysbiosis [ 39 ]. The identification of fungal and bacterial co-infections has crucial implications for clinical management. mNGS offers a unique advantage in this context, as it allows for the longitudinal monitoring of dynamic changes in pathogenic microorganisms within BALF and sputum samples. This enhances the diagnosis of co-infections and enables more targeted and effective clinical interventions. Furthermore, our findings unveiled notable dynamic changes in pathogen detection results across various time points, aligning with previous observations [ 41 , 42 ]. In eight cases with sequential sampling, Candida albicans emerged as the most frequently detected fungus, followed by Candida glabrata , Corynebacterium striatum , Acinetobacter baumannii , Escherichia coli , Stenotrophomonas maltophilia , and Pseudomonas aeruginosa . Human betaherpesvirus 5 and Human alphaherpesvirus 1 were the predominant viruses identified. Fluctuations in inflammatory marker levels, such as C-reactive protein, correlated with disease progression, and the community structure of respiratory pathogens exhibited variability among cases, highlighting the microbial diversity present. With the continuous advancement of mNGS technology, direct and semi-quantitative monitoring based on this approach has become a powerful tool for researchers to screen and compare microbial compositions and variants [ 41 , 43 ]. The lung microbiome not only influences susceptibility to disease but is also impacted by disease activity or treatment [ 44 ]. Our study demonstrated that the pathogen spectrum shifted as patients' conditions improved when BALF and sputum mNGS were used to monitor different treatment stages. This highlights the potential of mNGS technology in dynamically tracking pathogen sequences during treatment to assess disease progression and treatment efficacy. By leveraging the advantages of mNGS in pathogen detection, clinicians can tailor antibiotic management, thereby reducing mortality in ICU settings [ 45 ]. In a study by Xie et al. on severe pneumonia diagnosis in China, NGS was found to provide faster and more accurate diagnosis compared to conventional methods [ 46 ]. Early diagnosis through mNGS allowed for the adjustment of clinical treatment regimens based on results, leading to decreased 28-day and 90-day mortality rates in severe pneumonia patients [ 46 ]. In conclusion, the longitudinal examination of the lung microbiome in severe lower respiratory tract infection patients using mNGS offers invaluable insights for the development of personalized precision medicine. This study encompasses several limitations. Firstly, its retrospective nature may introduce inherent biases. Secondly, the patients had undergone antibiotic treatment prior to the collection of BALF or sputum samples, which could potentially skew the results, leading to discrepancies between negative findings from traditional culture testing and mNGS testing. Thirdly, the interpretative challenge of distinguishing pathogens from colonizers or contaminants is inherent in sputum-based mNGS, despite our use of background subtraction and RPM thresholds. Furthermore, the high cost of sequencing presents a significant constraint. Additionally, mNGS technology itself is not without limitations, including its susceptibility to interference from host nucleic acids and background pathogenic microorganisms. Finally, the absence of standardized interpretation for pathogenic microbial reports means that mNGS cannot fully replace conventional detection techniques but instead functions as a complementary diagnostic tool. CONCLUSION In this study, we employed mNGS technology to explore the pathogenic spectrum and its dynamic fluctuations in clinical samples obtained from adult patients with severe pneumonia in ICU. Our results demonstrate that mNGS outperforms traditional microbial detection techniques, effectively identifying a wide range of pathogens, especially those fungi that are difficult to culture, and exhibiting distinct advantages in diagnosing co-infections. Furthermore, mNGS technology enabled the dynamic monitoring of pathogen load and disease progression through semi-quantitative analysis. By generating spatiotemporal epidemiological maps of pathogenic microorganisms, we observed a notable correlation between the detected pathogens and patient outcomes. These findings provide a solid experimental foundation for the surveillance and clinical management of patients in ICU. Looking forward, several strategies can be implemented to enhance the application of mNGS technology in lower respiratory tract infections. These include optimizing mNGS technology, selecting more appropriate pretreatment methods and sequencing platforms, and developing a comprehensive data analysis platform. These measures can collectively reduce costs, enhance detection sensitivity, and assist clinicians in interpreting results. Abbreviations mNGS: Metagenomic Next-Generation Sequencing ICU: Intensive Care Unit BALF: Bronchoalveolar Lavage Fluid LRTIs: Lower Respiratory Tract Infections CAP: Community-Acquired Pneumonia HAP: Hospital-Acquired Pneumonia VAP: Ventilator-Associated Pneumonia PCR: Polymerase Chain Reaction WBC: White Blood Cell Count CRP: C-Reactive Protein PCT: Procalcitonin RPM: Reads Per Million NTC: No-Template Control MDR: Multi-Drug Resistant ARGs: Antibiotic Resistance Genes Declarations Clinical trial number Not applicable. Data availability All data generated in the study are available with the corresponding author on reasonable demand. Acknowledgements We sincerely thank all participants in this study and acknowledge the Department of Respiratory and Critical Care Medicine of the First Affiliated Hospital of Guangxi Medical University and the National Institute for Viral Disease Control and Prevention, Chinese CDC, for their support and collaboration in this research. Funding This work was supported by the National Key Research and Development Program of China (2021YFC2300101). Author information Authors and Affiliations NHC Key Laboratory of Biosafety, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing , China Zhen Li, Changcheng Wu, Yinjie Liang, Yuqian Zhai, Chen Mai, Yize Han, Wenling Wang, Wenjie Tan Collaborative Innovation Centre of Regenerative Medicine and Medical BioResourse Development and Application Co-constructed by the Province and Ministry, Guangxi Medical University, Nanning, Guangxi, China Zhen Li, Yinjie Liang, Chuanyi Ning The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China Debin Huang, Li_an Tang School of Nursing, Guangxi Medical University, Nanning, Guangxi, China Chuanyi Ning Guangxi Crucial Laboratory of AIDS Prevention and Treatment & School of Public Health, Guangxi Medical University, Nanning, Guangxi, China Chen Mai Contributions All authors contributed to the study conception and design. Material preparation, sample collection, and clinical data acquisition were performed by Zhen Li, Changcheng Wu, Debin Huang, Yinjie Liang, Li_an Tang, Yuqian Zhai, Chen Mai, and Yize Han. Laboratory testing, mNGS experiments, and bioinformatic analysis were conducted by Zhen Li, Yuqian Zhai, Chen Mai, and Yize Han. Data organization and statistical analysis were carried out by Zhen Li and Changcheng Wu. The first draft of the manuscript was written by Zhen Li and revised by Wenling Wang, Chuanyi Ning, and Wenjie Tan. All authors commented on previous versions of the manuscript and approved the final version for submission. Corresponding author Correspondence to Wenling Wang, Chuanyi Ning, and Wenjie Tan. Ethics declarations Ethics approval and consent to participate Written informed consent was obtained from all participants or their legally authorized representatives. The study protocol was reviewed and approved by the Ethics Committee of the National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention (approval no. IVDC2022-001). All procedures performed in this study were in accordance with the ethical standards of the Declaration of Helsinki. Consent for publication Not applicable. Competing interests The authors declare no competing interests. References Collaborators GL: Estimates of the global, regional, and national morbidity, mortality, and aetiologies of lower respiratory tract infections in 195 countries: a systematic analysis for the Global Burden of Disease Study 2015. Lancet Infect Dis 2017, 17(11):1133-1161. Lozano R, Naghavi M, Foreman K, Lim S, Shibuya K, Aboyans V, Abraham J, Adair T, Aggarwal R, Ahn SY et al : Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010. 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Table Table 1 Demographic and clinical characteristics of severe pneumonia cases Variable Total (n=25) Survivors (n=20) Non-survivors (n=5) χ2/t/z P value Age 66.64±15.23 66.90±16.33 65.60±11.06 0.17 0.87 Male, n (%) 18 (72) 15 (83.30) 3 (16.70) 0.45 0.60 Laboratory tests White blood cells, ×10 9 /L 12.05 (9.82, 17.81) 11.90 (9.74，15.13) 16.77 (10.06, 25.52) -0.82 0.45 Red blood cells, ×10 9 /L 3.28±0.85 3.30±0.80 3.21±1.10 0.22 0.83 Hemoglobin (g/L) 90.02±19.01 91.10±19.03 85.68±20.45 0.56 0.58 Platelet(×10 9 /L) 125.80 (53.25, 225.45) 207 (73.35, 243.08) 44 (28, 95.80) -2.38 0.02* Neutrophils , n (%) 87.00 (78.44, 92.66) 87.00 (79.04, 90.81) 86.95 (46.60, 94.38) -0.18 0.90 Lymphocytes, n (%) 5.26 (3.84, 10.59) 7.04 (3.97, 10.80) 4.30 (1.15, 5.22) -1.67 0.10 Procalcitonin, ng/ml 2.10 (0.62，5.20) 1.69 (0.54，3.66) 5.67 (3.08，19.47) -2.11 0.04* C-reactive protein, mg/L 117.3±54.84 112.51±56.15 136.46±49.93 -0.87 0.39 Underlying diseases, n (%) Diabetes 5 (20) 5 (100) 0 (0) 1.56 0.54 Hypertension 14 (56) 11 (78.60) 3 (21.40) 0.04 1.00 Complication, n (%) Sepsis/septic shock 13 (52) 11 (84.6) 2 (15.4) 0.36 0.65 Fever, n (%) 8 (32) 8 (100) 0 (0) 2.94 0.14 Mechanical ventilation, n (%) 20 (80) 16 (80) 4 (20) 0.00 1.00 History of smoking, n (%) 8 (32) 7 (87.50) 1 (12.50) 0.41 1.00 ICU time, n (%) 10 to 30 days 13 (52) 11 (55) 2 (40) 9.74 0.008* Additional Declarations No competing interests reported. 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11:59:05","extension":"html","order_by":28,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":164113,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8552663/v1/4567a19d50ee211b8c3ee0c3.html"},{"id":100400872,"identity":"67574c8d-adc0-40f0-8a11-da4b3e7c39e2","added_by":"auto","created_at":"2026-01-16 11:58:28","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":218524,"visible":true,"origin":"","legend":"\u003cp\u003eComparative Analysis of Pathogen Detection Results Obtained Using mNGS Versus Conventional Culture Methods.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8552663/v1/e7b54720dfa6342ee66448c7.jpg"},{"id":100399543,"identity":"7579ca65-1e3a-43de-a905-d4131360c695","added_by":"auto","created_at":"2026-01-16 11:57:12","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1461830,"visible":true,"origin":"","legend":"\u003cp\u003eComprehensive Analysis of Mixed Pathogenic Microbial Infections Utilizing mNGS.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8552663/v1/1bb73fe09288e4a9fedb32a8.jpg"},{"id":100401193,"identity":"8005e233-4985-4c2a-99ef-27ad43f4d7b4","added_by":"auto","created_at":"2026-01-16 11:58:45","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1336151,"visible":true,"origin":"","legend":"\u003cp\u003eDynamic Surveillance of Pathogenic Spectrum Variations and Inflammatory Indices in Lower Respiratory Tract Samples of Severe Pneumonia Patients Utilizing mNGS (Cases 1-8). Line plots show the trends of White Blood Cell count (WBC), Procalcitonin (PCT), and C-reactive protein (CRP) over days since ICU admission. Stacked bar plots show the relative abundance of bacterial (blue shades), fungal (purple shades), and viral (red shades) communities at each time point.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8552663/v1/3abdde73a0a8b07806108f77.jpg"},{"id":100401224,"identity":"8125a774-aede-4619-85a8-2ab5e49861f4","added_by":"auto","created_at":"2026-01-16 11:58:47","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1076781,"visible":true,"origin":"","legend":"\u003cp\u003eVariations in Species Diversity and Relative Abundance were Noted in the Sequential Samples. A) Bar chart illustrating the temporal changes in the Shannon diversity index for each case (1-8). B) Heatmap showing the detection frequency of the most commonly identified pathogens across the eight longitudinally monitored cases.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8552663/v1/0b8116c6ad754f532ed374eb.jpg"},{"id":105223487,"identity":"fe0c054d-fe45-4625-afcb-521168a04eea","added_by":"auto","created_at":"2026-03-23 16:07:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5827205,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8552663/v1/392e9215-ff49-4494-af00-eea30a7fd8ed.pdf"},{"id":100400530,"identity":"da099ea6-f505-4279-a23a-2c52dd0a7e79","added_by":"auto","created_at":"2026-01-16 11:58:11","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":26410,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8552663/v1/a98bc1868fdfeaa1dca51cb0.xlsx"},{"id":100400532,"identity":"b3cdd202-0d8e-4e58-a9eb-2146323e978e","added_by":"auto","created_at":"2026-01-16 11:58:11","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":13403,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8552663/v1/5b8d93ad51ec453bef4a8efe.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Metagenomics Reveals Pathogenic Diversity and Temporal Dynamics in Severe Pneumonia Among Patients in Adult Intensive Care Unit","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eRespiratory tract infections have consistently been a major focus in global public health, posing enduring challenges [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Lower respiratory tract infections (LRTIs) contribute significantly to the global disease burden, ranking as the second leading cause [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. LRTIs encompass various conditions, including community-acquired pneumonia (CAP), hospital-acquired pneumonia (HAP), ventilator-associated pneumonia (VAP), acute bronchitis, bronchiolitis, and bronchiectasis [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Severe lung infections are a primary cause of mortality from infectious diseases [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Severe pneumonia is typically caused by a diverse array of pathogens, including bacteria, viruses, and fungi [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Given its rapid onset, swift progression, complex etiology, and wide range of pathogens, timely identification of the causative pathogens is crucial for effective clinical intervention [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Traditional pathogen detection methods, such as smear analysis, histopathological examination, culture, serum antibody tests, and polymerase chain reaction (PCR), have limitations in terms of timeliness, specificity, and sensitivity, falling short of modern diagnostic requirements [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. While PCR offers rapid and specific detection of predefined pathogens, mNGS provides an unbiased approach capable of detecting a wider range of pathogens, including novel or unexpected taxa.\u003c/p\u003e \u003cp\u003eIn recent years, metagenomic next-generation sequencing (mNGS) has risen to prominence as a powerful tool in the diagnosis and treatment of infectious diseases, thanks to its efficient and broad-spectrum pathogen identification capabilities [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The unique advantage of mNGS is its untargeted nature, allowing the detection of pathogens without prior knowledge, solely through high-throughput sequencing of clinical samples [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This method enables comprehensive and relatively timely pathogen detection, making it particularly suitable for acute, critical, and complex clinical scenarios [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Despite significant advancements in clinical pathogen detection, there is still a lack of research on the dynamic changes in pathogen spectra in severe lower respiratory tract infections using mNGS. Understanding the dynamic evolution of pathogen spectra in adult ICU patients with severe pneumonia is crucial for treatment planning and adjustment. Therefore, we utilized mNGS to investigate the dynamic changes in pathogen spectra across a series of samples from patients with severe, persistent respiratory tract infections. Additionally, we analyzed the correlation between pathogenic microorganisms and patient outcomes, aiming to provide data-driven support for optimizing clinical treatment strategies.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003e\u003cstrong\u003eStudy Design and Participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study enrolled 25 adult patients with severe pneumonia who were admitted to the Respiratory Intensive Care Unit (ICU) of the First Affiliated Hospital of Guangxi Medical University and the Fourth People\u0026apos;s Hospital of Nanning between December 2021 and July 2022. All patients satisfied the diagnostic criteria for severe pneumonia established by the Chinese Society of Respiratory Diseases and the American Thoracic Society. Individuals with incomplete medical histories, HIV, or tuberculosis were excluded from the study.\u003c/p\u003e\n\u003cp\u003eBronchoalveolar lavage fluid (BALF) or sputum samples were collected from all 25 patients with severe pneumonia. These samples were concurrently subjected to traditional clinical microbiology tests, multiplex PCR, and metagenomic next-generation sequencing (mNGS). Eight of these patients underwent serial sampling at multiple time points (every three days), resulting in the collection of 3 BALF and 24 sputum samples. Furthermore, 17 patients were sampled at a single time point, providing a total of 17 additional sputum samples. Detailed demographic information, sample types, and clinical characteristics for each patient are summarized in Supplementary Table S1.\u003c/p\u003e\n\u003cp\u003eThe study was approved by both the hospitals\u0026apos; ethics committees and the Chinese Center for Disease Control and Prevention Scientific Research Ethics Review Committee. Informed consent was obtained from each patient or their authorized representative prior to participation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Data Collection and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eMeasurement of Inflammatory Markers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical information was gathered from electronic medical records and encompassed a range of details including demographic characteristics, medical history, dates of onset and sampling, primary clinical manifestations, imaging findings, clinical diagnoses, results from traditional culture tests, blood laboratory test results obtained at the time of metagenomic next-generation sequencing (mNGS) sampling, and details of antimicrobial usage. All clinical data are compiled in Supplementary Table S1.\u003c/p\u003e\n\u003cp\u003eWhite blood cell count (WBC), C-reactive protein (CRP), and procalcitonin (PCT) levels were measured from peripheral blood samples collected contemporaneously with respiratory samples, using standard automated clinical analyzers in the hospital\u0026apos;s central laboratory.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultiplex PCR Detection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA 500 \u0026mu;l sample was collected and subjected to DNA/RNA extraction using the QIAamp cador Pathogen Mini Kit (Qiagen, USA). Subsequently, primary pathogen screening was conducted utilizing the respiratory pathogen multiplex detection kit (Shanghai Jienuo Biotechnology Co., Ltd.). This kit utilizes reverse transcription to generate cDNA from RNA, followed by multiplex PCR for the qualitative detection of 22 respiratory pathogens, encompassing 16 RNA viruses (including influenza A virus, influenza B virus, influenza A virus H1N1, respiratory syncytial virus A and B, parainfluenza virus -1/-2/-3/-4, coronavirus OC43, coronavirus NL63/HKU1, coronavirus 229E, rhinovirus, enterovirus, human metapneumovirus), 2 DNA viruses (adenovirus and bocavirus), and 4 bacterial species (\u003cem\u003eChlamydia pneumoniae\u003c/em\u003e, \u003cem\u003eMycoplasma pneumoniae\u003c/em\u003e, \u003cem\u003eLegionella pneumophila\u003c/em\u003e, \u003cem\u003eBordetella pertussis\u003c/em\u003e).\u003c/p\u003e\n\u003cp\u003eThe sample underwent an initial real-time PCR reaction on a PCR machine, following this specific protocol: 50\u0026deg;C for 10 minutes, 95\u0026deg;C for 2 minutes, followed by 40 cycles of 94\u0026deg;C for 20 seconds, 55\u0026deg;C for 20 seconds, and 72\u0026deg;C for 35 seconds. Subsequently, the reaction was transferred to the LightCycler480 II real-time PCR instrument, with the following parameters: 95\u0026deg;C for 2 minutes, followed by 10 cycles of 94\u0026deg;C for 15 seconds, 55\u0026deg;C for 15 seconds, and 72\u0026deg;C for 15 seconds; then 23 cycles of 94\u0026deg;C for 15 seconds, 50\u0026deg;C for 15 seconds, and 72\u0026deg;C for 15 seconds. The program concluded with a step at 95\u0026deg;C for 2 minutes, followed by a 90-second melting curve analysis ranging from 40\u0026deg;C to 90\u0026deg;C, with fluorescence detection occurring at a rate of 1\u0026deg;C/s. Finally, a 1-second cooling period at 37\u0026deg;C was included.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMetagenomic Sequencing and Data Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter following standard procedures for collecting sputum or BALF samples, viscous samples were liquefied using sputum digest at a 1:1 ratio (Sputasol method) and incubated at 37\u0026deg;C for 30 minutes to obtain a homogeneous solution. Subsequently, 500 \u0026mu;l of the sample was used for nucleic acid extraction with the QIAamp DNA microbiome kit (Qiagen, USA). Human host nucleic acids were removed using Benzonase (Qiagen, USA). The concentration of DNA was determined using Qubit dsDNA HS Assay Kits (Thermo Fisher, USA) on a Qubit 3.0 fluorometer (ABI, USA). The DNA was then diluted to 0.2 ng/\u0026mu;l, and library construction was performed according to the instructions provided in the Nextera XT DNA Library Preparation Kit (Illumina, USA). Finally, the prepared libraries were sent to Tianjin Nuohe Zhiyuan Bioinformatics Technology Co., Ltd., where they underwent metagenomic sequencing using the Novaseq-PE150 sequencer (Illumina, USA). To ensure the absence of contamination, a template-free control (DNase/RNase-free water) was included alongside the respiratory samples for quality control purposes during the mNGS process.\u003c/p\u003e\n\u003cp\u003eRaw sequencing reads were processed using a standardized bioinformatics pipeline. First, low-quality reads, adapter sequences, and short reads (\u0026lt;50 bp) were filtered using Fastp (v0.23.2). Human-derived reads were then identified and removed by alignment to the GRCh38 reference genome using BWA-MEM (v0.7.17). The remaining high-quality, non-human reads were classified for microbial taxonomy using Kraken2 (v2.1.2) with the Standard database, and abundance was estimated with Bracken (v2.6.0). Potential pathogens were identified based on a reads per million (RPM) threshold of \u0026gt;10, after subtraction of any reads also detected in no-template controls (NTCs), following the interpretive framework suggested in the mNGS quality control expert consensus..\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were performed using SPSS 22.0 (SPSS Inc., Chicago, IL, USA), GraphPad Prism 8 (GraphPad Software, Inc., La Jolla, CA, USA), and R 4.0 software. Continuous variables that adhered to a normal distribution were reported as the mean \u0026plusmn; standard deviation, whereas those with skewed distributions were described by the median and interquartile range. Categorical variables were presented as frequencies and percentages. Comparisons of continuous variables were conducted using either the t-test or the Mann-Whitney U test, depending on the data distribution. Categorical variables were compared using Pearson\u0026apos;s chi-square test or Fisher\u0026apos;s exact probability method, as appropriate. Statistical significance was determined based on a two-sided test with a \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003eDemographic and Clinical Characteristics of Severe Pneumonia Cases\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the inclusion criteria, this study encompassed 25 cases of severe pneumonia, comprising 18 males (72%) and 7 females (28%). Laboratory analyses revealed a white blood cell count of 12.05 (9.82, 17.81) \u0026times; 10^9/L, with a neutrophil percentage of 87.00 (78.44, 92.66)% and a lymphocyte percentage of 5.26 (3.84, 10.59)%. The procalcitonin level was 2.10 (0.62, 5.20) ng/mL, and the C-reactive protein level was 117.3 \u0026plusmn; 54.84 mg/L. Among the patients, 8 (32%) had fever, and the majority had underlying medical conditions: 14 (56%) had hypertension and 5 (20%) had diabetes mellitus. Furthermore, 13 (52%) patients developed sepsis/septic shock, 20 (80%) had a history of mechanical ventilation, and 8 (32%) had a smoking history. The length of hospital stay for 13 (52%) patients ranged from 10 to 30 days.\u003c/p\u003e\n\u003cp\u003eOverall, 20 (80%) patients survived, while 5 (20%) died. The mean age of discharged patients was 66.90 \u0026plusmn; 16.33 years, compared to 65.60 \u0026plusmn; 11.06 years for deceased patients. Significant differences were observed in platelet count and serum procalcitonin levels between the discharged and deceased patients (P \u0026lt; 0.05). Additionally, the discharged patients had a significantly longer hospital stay compared to the deceased patients (P \u0026lt; 0.01). These findings are summarized in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePreliminary Screening and Rationale for Metagenomic Sequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll collected respiratory samples were initially screened using the respiratory pathogen detection kit as described above. Notably, all samples tested negative for the 22 targeted respiratory pathogens (including 18 viruses and 4 bacteria). Given that the clinical presentation and history of the enrolled severe pneumonia patients were not typical for common viral respiratory infections, and considering the negative results from this broad targeted panel, we hypothesized that the causative agents might be outside its detection scope. To enable a comprehensive and hypothesis-free investigation, we proceeded to perform mNGS on all samples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparison of mNGS Results with Traditional Culture Assays\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe initially evaluated the diagnostic efficacy of mNGS and traditional culture methods in identifying pathogens, as depicted in Figure 1. Our study involved samples from 25 patients, from which a total of 53 pathogens were identified using both methodologies. These pathogens included 32 bacteria, 10 fungi, and 11 viruses. Overall, mNGS exhibited a wider range of microorganism detection compared to traditional culture techniques.\u003c/p\u003e\n\u003cp\u003eThe most commonly detected bacteria were Stenotrophomonas maltophilia, Corynebacterium striatum, Escherichia coli, Acinetobacter baumannii, and Klebsiella pneumoniae. Among the fungi, Candida albicans, Candida glabrata, Candida dubliniensis, and Candida orthopsilosis were frequently identified. Notably, mNGS demonstrated significantly higher detection rates for pathogens such as Pseudomonas aeruginosa, Klebsiella pneumoniae, Acinetobacter baumannii, Burkholderia cepacia, Candida glabrata, Candida albicans, and Aspergillus fumigatus compared to traditional culture methods (P \u0026lt; 0.001), as illustrated in Figure 1A and Figure 1B.\u003c/p\u003e\n\u003cp\u003eAdditionally, mNGS predominantly identified viruses such as Human alphaherpesvirus 1, Human betaherpesvirus 5, Human alphaherpesvirus 2, and Human gammaherpesvirus 4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePathogen Profiling of Respiratory Tract Samples\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUpon further analysis of the lower respiratory tract samples, we observed the presence of dominant species with relative abundances exceeding 30% in specific samples (see Figure 2). It should be noted that relative abundance is dependent on the sequencing depth and community composition; all samples met the minimum sequencing data requirement recommended for BALF/sputum analysis (\u0026gt;20 million reads). Notable examples include \u003cem\u003eCandida orthopsilosis\u003c/em\u003e in sample N1_2B, \u003cem\u003eCandida albicans\u003c/em\u003e in samples N5-1S, N6-1S, and N17-1S, \u003cem\u003eCandida glabrata\u003c/em\u003e in sample N17-1S, \u003cem\u003eAspergillus fumigatus\u003c/em\u003e in sample N25-1S, \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e in samples N5-2S, \u003cem\u003eCorynebacterium striatum\u003c/em\u003e in samples N3-1S, N4-1S, and N11-1S, \u003cem\u003eAcinetobacter baumannii\u003c/em\u003e in samples N1_1B, N1_3B, and N19-1S, \u003cem\u003eStaphylococcus aureus\u003c/em\u003e in sample N10-1S, \u003cem\u003eEscherichia coli\u003c/em\u003e in sample N2-4S, \u003cem\u003eStenotrophomonas maltophilia\u003c/em\u003e in samples N2-2S, N2-4S, and N18-1S, as well as in samples N3-2S and N5-3S. \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e was detected in sample N12-1S, while \u003cem\u003eBurkholderia cenocepacia\u003c/em\u003e was found in samples N2-2S, N2-3S, and N9-1S.\u003c/p\u003e\n\u003cp\u003eTo preliminarily explore associations between clinical status and microbial profiles, patients were stratified by clinical outcome (survivors vs. non-survivors) and length of ICU stay (\u0026lt;15 vs.\u0026nbsp;\u0026ge;15 days). While no statistically significant differences in the presence of specific dominant taxa were found (Fisher\u0026rsquo;s exact test, P \u0026gt; 0.05), likely due to small sample size, visual inspection suggested trends (e.g., higher relative abundance of \u003cem\u003eAcinetobacter baumannii\u003c/em\u003e in non-survivors). These observations warrant validation in larger cohorts.\u003c/p\u003e\n\u003cp\u003eThese findings suggest a potential association between the patient\u0026apos;s clinical condition and the abundance of microorganisms in the sample. Notably, there was a prevalence of nosocomial infections among the dominant species, such as \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e, \u003cem\u003eAcinetobacter baumannii\u003c/em\u003e, \u003cem\u003eEscherichia coli\u003c/em\u003e, and \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e, indicating that patients with prolonged antibiotic use or extended hospital stays may be more susceptible to these infections.\u003c/p\u003e\n\u003cp\u003eIn our study, a total of 44 lower respiratory tract samples were collected. Of these, 64% (28/44) were single-pathogen infections, while 36% (16/44) were mixed-pathogen infections. The mixed-pathogen infections comprised 13 cases of bacterial and fungal infections and 3 cases of bacterial and viral infections. It is important to note that our cohort primarily consisted of sputum samples (n=41), with only 3 BALF samples. While sputum may be subject to upper respiratory tract contamination, it remains a commonly used specimen in severe pneumonia. The interpretive challenges of differentiating colonization from infection in sputum are acknowledged, and findings should be considered in this context.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of Dynamic Changes in Pathogen Spectrum\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, we conducted serial metagenomic next-generation sequencing (mNGS) testing on eight cases of severe pneumonia to monitor the dynamic changes in pathogens throughout the disease course. Simultaneously, we analyzed variations in inflammatory markers, including leukocytes, procalcitonin, and C-reactive protein, to investigate the relationship between the clinical progression of severe pneumonia and shifts in the respiratory pathogen microbial profile (see Figure 3).\u003c/p\u003e\n\u003cp\u003eIn samples from Cases 1 and 5, the sequential alterations in fungal relative abundance paralleled the changes in white blood cell count and C-reactive protein, both showing a consistent decline. After the administration of effective anti-infective therapy, the detection of fungal sequences by mNGS diminished within two weeks and ultimately became undetectable. Similarly, leukocyte and procalcitonin levels also demonstrated a decreasing trend. In contrast, bacterial diversity and relative abundance gradually increased, suggesting an heightened risk of nosocomial infection with extended hospital stays (refer to Figures 3A and 3E for further details).\u003c/p\u003e\n\u003cp\u003eIn samples from Case 2, the persistent rise in bacterial species abundance and inflammatory markers indicated ineffective anti-infective therapy (refer to Figure 3B for further details). For Case 8, the abundance of fungal species continued to decrease in concordance with changes in C-reactive protein, while the abundance of bacterial species initially decreased and subsequently increased, mirroring the trend observed in leukocytes (refer to Figure 3H for further details).\u003c/p\u003e\n\u003cp\u003eIn the eight severe pneumonia cases where serial sampling was conducted, the microbial profiles displayed diverse trends (Fig. 3). Specifically, species diversity declined in Cases 1 and 3, while it rose subsequently in Cases 2 and 5. Case 6 exhibited an upward trend, and Case 8 demonstrated a fluctuating pattern of increase and decrease, as illustrated in Figure 4A. Interestingly, the Shannon diversity index did not show a consistent decline during hospitalization despite antibiotic exposure. The analysis of dynamic pathogen detection results across these eight cases identified \u003cem\u003eCandida albicans\u003c/em\u003e as the most consistently detected fungus, followed by \u003cem\u003eCandida glabrata\u003c/em\u003e, \u003cem\u003eCorynebacterium striatum\u003c/em\u003e, \u003cem\u003eAcinetobacter baumannii\u003c/em\u003e, \u003cem\u003eEscherichia coli\u003c/em\u003e, \u003cem\u003eStenotrophomonas maltophilia\u003c/em\u003e, and \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e. Additionally, Human alphaherpesvirus 1 and Human betaherpesvirus 5 were the most prevalent viruses detected, as depicted in Figure 4B.\u003c/p\u003e\n\u003cp\u003eThese findings reveal varying levels of species diversity and relative abundance in consecutive samples, which may be correlated with disease progression. Notably, patients with severe pneumonia exhibited a predominance of bacterial species such as \u003cem\u003eCandida orthopsilosis\u003c/em\u003e, \u003cem\u003eCandida albicans\u003c/em\u003e, \u003cem\u003eCandida glabrata\u003c/em\u003e, \u003cem\u003eCorynebacterium striatum\u003c/em\u003e, \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e, \u003cem\u003eAcinetobacter baumannii\u003c/em\u003e, \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e, and Human alphaherpesvirus 1. The majority of these species are associated with colonization or hospital-acquired infections, indicating that patients with prolonged antibiotic use or hospital stays may be particularly susceptible to nosocomial infections.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this study, we utilized mNGS to analyze the pathogenic spectrum in patients with severe pneumonia, revealing dynamic fluctuations in pathogens. Initially, we compared the results of mNGS with traditional pathogen cultures while examining demographic characteristics, laboratory findings, underlying medical conditions, complications, and lengths of hospital stay among the severe pneumonia cases. Consistent with previous studies, mNGS demonstrated enhanced sensitivity and accuracy in pathogen identification compared to traditional culture methods, offering a broader range of pathogen detection and improved capabilities for detecting co-infections [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Pan et al.'s research identified five cases of bacterial, fungal, or viral co-infections through mNGS, allowing clinicians to conduct more comprehensive patient assessments and implement effective treatment strategies [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Similarly, Peng et al.'s findings indicated a high prevalence of polymicrobial infections among immunocompromised patients with suspected pneumonia, with mNGS demonstrating comparable overall diagnostic performance to conventional microbial testing as a primary diagnostic tool [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Therefore, in cases involving rare pathogens or limitations in routine microbial testing, a combined approach utilizing both mNGS and conventional microbial assays may constitute the optimal diagnostic strategy in clinical practice [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSecondly, we analyzed the pathogenic spectrum in lower respiratory tract samples collected from 44 ICU patients diagnosed with severe pneumonia. Our findings revealed that the three most prevalent bacteria were \u003cem\u003eStenotrophomonas maltophilia\u003c/em\u003e (61.36%), \u003cem\u003eCorynebacterium striatum\u003c/em\u003e (54.55%), and \u003cem\u003eEscherichia coli\u003c/em\u003e (54.55%). Among the identified pathogenic bacteria, Gram-positive species included \u003cem\u003eCorynebacterium striatum\u003c/em\u003e, \u003cem\u003eStaphylococcus epidermidis\u003c/em\u003e, \u003cem\u003eStaphylococcus aureus\u003c/em\u003e, \u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e, and \u003cem\u003eEnterococcus faecium\u003c/em\u003e, while Gram-negative bacteria comprised \u003cem\u003eEscherichia coli\u003c/em\u003e, \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e, \u003cem\u003eStenotrophomonas maltophilia\u003c/em\u003e, \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e, and \u003cem\u003eAcinetobacter baumannii\u003c/em\u003e. These results are consistent with those reported by Chien et al. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. (A complete list of detected taxa is provided in Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, our study observed an increase in the detection of pathogens, such as \u003cem\u003eAcinetobacter\u003c/em\u003e and \u003cem\u003eCorynebacterium\u003c/em\u003e, with prolonged hospital stays and continued antibiotic administration. Notably, certain pathogens emerged as dominant, including \u003cem\u003eCorynebacterium striatum\u003c/em\u003e, \u003cem\u003eAcinetobacter baumannii\u003c/em\u003e, and \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e. This highlights the susceptibility of our patients to nosocomial infections due to extended antibiotic usage and prolonged hospital stays [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In recent years, \u003cem\u003eCorynebacterium striatum\u003c/em\u003e has emerged as a multidrug-resistant pathogen capable of causing nosocomial outbreaks [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e, a common opportunistic pathogen, is a significant contributor to nosocomial infections and exhibits a notable level of multi-drug resistance [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. \u003cem\u003eAcinetobacter baumannii\u003c/em\u003e has gained prominence as a key pathogen causing various infections, including pneumonia, bloodstream infections, skin and soft tissue infections, and urinary tract infections, leading to increased morbidity and mortality rates [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In immunocompromised patients, \u003cem\u003eAcinetobacter baumannii\u003c/em\u003e poses a significantly higher pathogenic risk [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Globally, 45% of \u003cem\u003eAcinetobacter baumannii\u003c/em\u003e isolates are classified as multi-drug resistant pathogens (MDRs) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAn important limitation of our study is the absence of resistome analysis. The mNGS data generated here hold potential for profiling antibiotic resistance genes (ARGs), which, when correlated with antimicrobial treatment records, could provide direct insights into resistance selection pressure and treatment efficacy. The Chinese mNGS expert consensus outlines principles for reporting detected resistance genes alongside phenotypic correlation. Future analysis of these datasets for ARGs and their genomic context is warranted and would significantly enhance the clinical utility of mNGS in guiding antimicrobial stewardship.\u003c/p\u003e \u003cp\u003eAmong the viruses identified in this study, the three most prevalent were Human alphaherpesvirus 1 (31.82%), Human gammaherpesvirus 4 (27.27%), and Human alphaherpesvirus 2 (11.36%). These viruses primarily belong to the Human Herpesvirus genus, with Human alphaherpesvirus 1, Human alphaherpesvirus 2, Human gammaherpesvirus 4, and Human betaherpesvirus 5 being the most frequently encountered variants.\u003c/p\u003e \u003cp\u003eIn this study, \u003cem\u003eCandida albicans\u003c/em\u003e (accounting for 50%) and \u003cem\u003eCandida glabrata\u003c/em\u003e (27.27%) emerged as the most prevalent fungal species detected. The rise of fungal pathogen infections poses a growing concern for global public health [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In February 2023, the World Health Organization unveiled its first-ever list of priority fungal pathogens, emphasizing the significance of \u003cem\u003eCryptococcus neoformans\u003c/em\u003e, various \u003cem\u003eCandida\u003c/em\u003e species, \u003cem\u003eAspergillus fumigatus\u003c/em\u003e, and \u003cem\u003eCandida albicans\u003c/em\u003e [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Over the past few decades, there has been a notable increase in fungal lung infections among immunocompromised patients [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Our study cohort primarily consisted of elderly individuals with pre-existing medical conditions, indicating compromised immune systems and heightened vulnerability to opportunistic lung pathogens.\u003c/p\u003e \u003cp\u003eA survey of oral fungal communities in healthy individuals revealed that 36.1% of fungal species were undetectable using conventional culture methods [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In contrast to the time-consuming and challenging nature of fungal cultures, mNGS has emerged as an unbiased, efficient, and rapid diagnostic tool for pathogen identification, effectively overcoming current diagnostic limitations [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. According to Miao et al., mNGS demonstrated superior performance in fungal detection compared to traditional methods, with an odds ratio of 4.0 (95% CI, 1.6\u0026ndash;10.3; P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study also identified a variety of fungi, with \u003cem\u003eCandida\u003c/em\u003e species, particularly \u003cem\u003eCandida albicans\u003c/em\u003e and \u003cem\u003eCandida glabrata\u003c/em\u003e, being the most common. \u003cem\u003eCandida\u003c/em\u003e infections are highly prevalent in immunocompromised patients and are associated with a high fatality rate ranging from 40\u0026ndash;60% [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Additionally, Aspergillus fumigatus, a common pathogenic fungus, was also detected [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Invasive pulmonary aspergillosis remains a major cause of death among immunocompromised patients [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNotably, our serial monitoring data showed a significant reduction in fungal abundance among patients receiving antifungal treatment, correlating with laboratory improvements. This underscores the crucial role of clinicians in actively targeting pathogens during therapy. In conclusion, mNGS holds considerable clinical value in the precise diagnosis of pulmonary fungal infections.\u003c/p\u003e \u003cp\u003eIn the study, it was found that 40.3% of lung fungal infection cases involved mixed fungal and bacterial infections [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Specifically, 36% (16 out of 44) of these mixed-pathogen infections were predominantly bacterial. A weakened immune system emerged as a significant risk factor for bacterial-fungal co-infections, and the presence of inflammatory lung lesions further increased susceptibility to such infections [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Additionally, the administration of multiple antibiotics suggested the presence of bacterial or more complex infections. Prolonged antibiotic use, defined as use for 14 days or more, not only promotes drug resistance but also disrupts the body's normal microbial flora, leading to microbial dysbiosis [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe identification of fungal and bacterial co-infections has crucial implications for clinical management. mNGS offers a unique advantage in this context, as it allows for the longitudinal monitoring of dynamic changes in pathogenic microorganisms within BALF and sputum samples. This enhances the diagnosis of co-infections and enables more targeted and effective clinical interventions.\u003c/p\u003e \u003cp\u003eFurthermore, our findings unveiled notable dynamic changes in pathogen detection results across various time points, aligning with previous observations [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In eight cases with sequential sampling, \u003cem\u003eCandida albicans\u003c/em\u003e emerged as the most frequently detected fungus, followed by \u003cem\u003eCandida glabrata\u003c/em\u003e, \u003cem\u003eCorynebacterium striatum\u003c/em\u003e, \u003cem\u003eAcinetobacter baumannii\u003c/em\u003e, \u003cem\u003eEscherichia coli\u003c/em\u003e, \u003cem\u003eStenotrophomonas maltophilia\u003c/em\u003e, and \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e. Human betaherpesvirus 5 and Human alphaherpesvirus 1 were the predominant viruses identified. Fluctuations in inflammatory marker levels, such as C-reactive protein, correlated with disease progression, and the community structure of respiratory pathogens exhibited variability among cases, highlighting the microbial diversity present.\u003c/p\u003e \u003cp\u003eWith the continuous advancement of mNGS technology, direct and semi-quantitative monitoring based on this approach has become a powerful tool for researchers to screen and compare microbial compositions and variants [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The lung microbiome not only influences susceptibility to disease but is also impacted by disease activity or treatment [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Our study demonstrated that the pathogen spectrum shifted as patients' conditions improved when BALF and sputum mNGS were used to monitor different treatment stages. This highlights the potential of mNGS technology in dynamically tracking pathogen sequences during treatment to assess disease progression and treatment efficacy.\u003c/p\u003e \u003cp\u003eBy leveraging the advantages of mNGS in pathogen detection, clinicians can tailor antibiotic management, thereby reducing mortality in ICU settings [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. In a study by Xie et al. on severe pneumonia diagnosis in China, NGS was found to provide faster and more accurate diagnosis compared to conventional methods [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Early diagnosis through mNGS allowed for the adjustment of clinical treatment regimens based on results, leading to decreased 28-day and 90-day mortality rates in severe pneumonia patients [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn conclusion, the longitudinal examination of the lung microbiome in severe lower respiratory tract infection patients using mNGS offers invaluable insights for the development of personalized precision medicine.\u003c/p\u003e \u003cp\u003eThis study encompasses several limitations. Firstly, its retrospective nature may introduce inherent biases. Secondly, the patients had undergone antibiotic treatment prior to the collection of BALF or sputum samples, which could potentially skew the results, leading to discrepancies between negative findings from traditional culture testing and mNGS testing. Thirdly, the interpretative challenge of distinguishing pathogens from colonizers or contaminants is inherent in sputum-based mNGS, despite our use of background subtraction and RPM thresholds. Furthermore, the high cost of sequencing presents a significant constraint. Additionally, mNGS technology itself is not without limitations, including its susceptibility to interference from host nucleic acids and background pathogenic microorganisms. Finally, the absence of standardized interpretation for pathogenic microbial reports means that mNGS cannot fully replace conventional detection techniques but instead functions as a complementary diagnostic tool.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eIn this study, we employed mNGS technology to explore the pathogenic spectrum and its dynamic fluctuations in clinical samples obtained from adult patients with severe pneumonia in ICU. Our results demonstrate that mNGS outperforms traditional microbial detection techniques, effectively identifying a wide range of pathogens, especially those fungi that are difficult to culture, and exhibiting distinct advantages in diagnosing co-infections. Furthermore, mNGS technology enabled the dynamic monitoring of pathogen load and disease progression through semi-quantitative analysis. By generating spatiotemporal epidemiological maps of pathogenic microorganisms, we observed a notable correlation between the detected pathogens and patient outcomes. These findings provide a solid experimental foundation for the surveillance and clinical management of patients in ICU.\u003c/p\u003e \u003cp\u003eLooking forward, several strategies can be implemented to enhance the application of mNGS technology in lower respiratory tract infections. These include optimizing mNGS technology, selecting more appropriate pretreatment methods and sequencing platforms, and developing a comprehensive data analysis platform. These measures can collectively reduce costs, enhance detection sensitivity, and assist clinicians in interpreting results.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003emNGS:\u003c/strong\u003e Metagenomic Next-Generation Sequencing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eICU:\u003c/strong\u003e Intensive Care Unit\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBALF:\u003c/strong\u003e Bronchoalveolar Lavage Fluid\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLRTIs:\u0026nbsp;\u003c/strong\u003eLower Respiratory Tract Infections\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCAP:\u003c/strong\u003e Community-Acquired Pneumonia\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHAP:\u003c/strong\u003e Hospital-Acquired Pneumonia\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVAP:\u003c/strong\u003e Ventilator-Associated Pneumonia\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePCR:\u003c/strong\u003e Polymerase Chain Reaction\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWBC:\u003c/strong\u003e White Blood Cell Count\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRP:\u003c/strong\u003e C-Reactive Protein\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePCT:\u003c/strong\u003e Procalcitonin\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRPM:\u003c/strong\u003e Reads Per Million\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNTC:\u003c/strong\u003e No-Template Control\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMDR:\u003c/strong\u003e Multi-Drug Resistant\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eARGs:\u003c/strong\u003e Antibiotic Resistance Genes\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated in the study are available with the corresponding author on reasonable demand.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely thank all participants in this study and acknowledge the Department of Respiratory and Critical Care Medicine of the First Affiliated Hospital of Guangxi Medical University and the National Institute for Viral Disease Control and Prevention, Chinese CDC, for their support and collaboration in this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Key Research and Development Program of China (2021YFC2300101).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNHC Key Laboratory of Biosafety, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing , China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZhen Li, Changcheng Wu, Yinjie Liang, Yuqian Zhai, Chen Mai, Yize Han, Wenling Wang, Wenjie Tan\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCollaborative Innovation Centre of Regenerative Medicine and Medical BioResourse Development and Application Co-constructed by the Province and Ministry, Guangxi Medical University, Nanning, Guangxi, China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZhen Li, Yinjie Liang, Chuanyi Ning\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDebin Huang, Li_an Tang\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSchool of Nursing, Guangxi Medical University, Nanning, Guangxi, China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChuanyi Ning\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGuangxi Crucial Laboratory of AIDS Prevention and Treatment \u0026amp; School of Public Health, Guangxi Medical University, Nanning, Guangxi, China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChen Mai\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, sample collection, and clinical data acquisition were performed by Zhen Li, Changcheng Wu, Debin Huang, Yinjie Liang, Li_an Tang, Yuqian Zhai, Chen Mai, and Yize Han. Laboratory testing, mNGS experiments, and bioinformatic analysis were conducted by Zhen Li, Yuqian Zhai, Chen Mai, and Yize Han. Data organization and statistical analysis were carried out by Zhen Li \u0026nbsp;and Changcheng Wu. The first draft of the manuscript was written by Zhen Li and revised by Wenling Wang, Chuanyi Ning, and Wenjie Tan. All authors commented on previous versions of the manuscript and approved the final version for submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Wenling Wang, Chuanyi Ning, and Wenjie Tan.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from all participants or their legally authorized representatives. The study protocol was reviewed and approved by the Ethics Committee of the National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention (approval no. IVDC2022-001). All procedures performed in this study were in accordance with the ethical standards of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCollaborators GL: Estimates of the global, regional, and national morbidity, mortality, and aetiologies of lower respiratory tract infections in 195 countries: a systematic analysis for the Global Burden of Disease Study 2015. \u003cem\u003eLancet Infect Dis \u003c/em\u003e2017, 17(11):1133-1161.\u003c/li\u003e\n\u003cli\u003eLozano R, Naghavi M, Foreman K, Lim S, Shibuya K, Aboyans V, Abraham J, Adair T, Aggarwal R, Ahn 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Reveals Dynamic Changes of Whole Gut Microbiota in the Acute Phase of Intensive Care Unit Patients\u003c/strong\u003e. \u003cem\u003eDig Dis Sci \u003c/em\u003e2016, \u003cstrong\u003e61\u003c/strong\u003e(6):1628-1634.\u003c/li\u003e\n\u003cli\u003eAi JW, Zhang HC, Cui P, Xu B, Gao Y, Cheng Q, Li T, Wu H, Zhang WH: Dynamic and direct pathogen load surveillance to monitor disease progression and therapeutic efficacy in central nervous system infection using a novel semi-quantitive sequencing platform. \u003cem\u003eJ Infect \u003c/em\u003e2018, 76(3):307-310.\u003c/li\u003e\n\u003cli\u003eYagi K, Huffnagle GB, Lukacs NW, Asai N: \u003cstrong\u003eThe lung microbiome during health and disease\u003c/strong\u003e. \u003cem\u003eInt J Mol Sci \u003c/em\u003e2021, \u003cstrong\u003e22\u003c/strong\u003e(19):10872.\u003c/li\u003e\n\u003cli\u003eSun T, Wu X, Cai Y, Zhai T, Huang L, Zhang Y, Zhan Q: Metagenomic next-generation sequencing for pathogenic diagnosis and antibiotic management of severe community-acquired pneumonia in immunocompromised adults. \u003cem\u003eFront Cell Infect Microbiol \u003c/em\u003e2021, 11:661589.\u003c/li\u003e\n\u003cli\u003eXie Y, Du J, Jin W, Teng X, Cheng R, Huang P, Xie H, Zhou Z, Tian R, Wang R\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eNext generation sequencing for diagnosis of severe pneumonia: China, 2010-2018\u003c/strong\u003e. \u003cem\u003eJ Infect \u003c/em\u003e2019, \u003cstrong\u003e78\u003c/strong\u003e(2):158-169.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable 1 Demographic and clinical characteristics of severe pneumonia cases\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6495%;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003eTotal (n=25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5876%;\"\u003e\n \u003cp\u003eSurvivors (n=20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6495%;\"\u003e\n \u003cp\u003eNon-survivors (n=5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.21649%;\"\u003e\n \u003cp\u003e\u0026chi;2/t/z\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.27835%;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6495%;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e66.64\u0026plusmn;15.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e66.90\u0026plusmn;16.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e65.60\u0026plusmn;11.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.21649%;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6495%;\"\u003e\n \u003cp\u003eMale, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e18 (72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e15 (83.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e3 (16.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.21649%;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6495%;\"\u003e\n \u003cp\u003eLaboratory tests\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.21649%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6495%;\"\u003e\n \u003cp\u003eWhite blood cells, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e12.05 (9.82, 17.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e11.90 (9.74，15.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e16.77 (10.06, 25.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.21649%;\"\u003e\n \u003cp\u003e-0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6495%;\"\u003e\n \u003cp\u003eRed blood cells, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e3.28\u0026plusmn;0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e3.30\u0026plusmn;0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e3.21\u0026plusmn;1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.21649%;\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6495%;\"\u003e\n \u003cp\u003eHemoglobin (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e90.02\u0026plusmn;19.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e91.10\u0026plusmn;19.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e85.68\u0026plusmn;20.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.21649%;\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6495%;\"\u003e\n \u003cp\u003ePlatelet(\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e125.80 (53.25, 225.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e207 (73.35, 243.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e44 (28, 95.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.21649%;\"\u003e\n \u003cp\u003e-2.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.02*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6495%;\"\u003e\n \u003cp\u003eNeutrophils , n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e87.00 (78.44, 92.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e87.00 (79.04, 90.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e86.95 (46.60, 94.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.21649%;\"\u003e\n \u003cp\u003e-0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6495%;\"\u003e\n \u003cp\u003eLymphocytes, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e5.26 (3.84, 10.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e7.04 (3.97, 10.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e4.30 (1.15, 5.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.21649%;\"\u003e\n \u003cp\u003e-1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6495%;\"\u003e\n \u003cp\u003eProcalcitonin, ng/ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e2.10 (0.62，5.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e1.69 (0.54，3.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e5.67 (3.08，19.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.21649%;\"\u003e\n \u003cp\u003e-2.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.04*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6495%;\"\u003e\n \u003cp\u003eC-reactive protein, mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e117.3\u0026plusmn;54.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e112.51\u0026plusmn;56.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e136.46\u0026plusmn;49.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.21649%;\"\u003e\n \u003cp\u003e-0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6495%;\"\u003e\n \u003cp\u003eUnderlying diseases, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.21649%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6495%;\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e5 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e5 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.21649%;\"\u003e\n \u003cp\u003e1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6495%;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e14 (56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e11 (78.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e3 (21.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.21649%;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6495%;\"\u003e\n \u003cp\u003eComplication, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.21649%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6495%;\"\u003e\n \u003cp\u003eSepsis/septic shock\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e13 (52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e11 (84.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e2 (15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.21649%;\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6495%;\"\u003e\n \u003cp\u003eFever, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e8 (32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e8 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.21649%;\"\u003e\n \u003cp\u003e2.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6495%;\"\u003e\n \u003cp\u003eMechanical ventilation, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e20 (80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e16 (80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e4 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.21649%;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6495%;\"\u003e\n \u003cp\u003eHistory of smoking, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e8 (32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e7 (87.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e1 (12.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.21649%;\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6495%;\"\u003e\n \u003cp\u003eICU time, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.21649%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e10 to 30 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e13 (52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e11 (55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e2 (40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.21649%;\"\u003e\n \u003cp\u003e9.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.008*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Metagenomic next-generation sequencing (mNGS), Intensive Care Unit(ICU); Severe pneumonia, Respiratory tract, Dynamic monitoring","lastPublishedDoi":"10.21203/rs.3.rs-8552663/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8552663/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMetagenomic next-generation sequencing (mNGS) emerging as a standout in the clinical setting. In this study, we harnessed the power of mNGS to explore the pathogenic spectrum and temporal variations in respiratory tract specimens collected from adult patients with severe pneumonia in the Intensive Care Unit (ICU) of a hospital in Guangxi.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eFrom December 2021 to July 2022, 44 respiratory tract samples (including sputum and bronchoalveolar lavage fluid ) from 25 adult patients(comprising 18 males and 7 females) diagnosed with severe pneumonia and admitted to the ICUs of two hospitals in Guangxi. A customized mNGS detection protocol was developed and applied for analyzing the composition and temporal variations of pathogens within the respiratory tract samples.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong these patients, the bacteria, fungi, and viruses were markedly higher detected by mNGS compared to conventional microbial culture methods (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The most prevalent bacteria detected were \u003cem\u003eStenotrophomonas maltophilia\u003c/em\u003e (61.36%), \u003cem\u003eCorynebacterium striatum\u003c/em\u003e (54.55%), and \u003cem\u003eEscherichia coli\u003c/em\u003e (54.55%). The viruses with the highest detection rates were human herpesviruses(HSV-1, 31.82%;HCMV, 27.27%;HSV-2, 11.36%). The most frequently identified fungi were \u003cem\u003eCandida albicans\u003c/em\u003e (50%) and \u003cem\u003eCandida glabrata\u003c/em\u003e (27.27%). Single-pathogen infections accounted for 64% (28/44) of the cases, while mixed-pathogen infections comprised 36% (16/44). Dynamic monitoring using mNGS in 8 patients uncovered diverse respiratory pathogenic spectra, with the majority of patients exhibiting dynamic changes that correlated with fluctuations in inflammatory markers such as leukocyte counts, procalcitonin levels, and C-reactive protein levels, alongside the clinical progression of the disease.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003emNGS exhibits superior performance in diagnosing mixed infections and real-time tracking of the pathogen spectrum, which provide a robust empirical basis for guiding clinical diagnosis and treatment strategies of patients in ICU.\u003c/p\u003e","manuscriptTitle":"Metagenomics Reveals Pathogenic Diversity and Temporal Dynamics in Severe Pneumonia Among Patients in Adult Intensive Care Unit","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-16 09:03:04","doi":"10.21203/rs.3.rs-8552663/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision 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Infectious Diseases","date":"2026-01-08T14:11:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"73d55c2d-e643-403a-8c28-2041300ecf7e","owner":[],"postedDate":"January 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-23T16:03:44+00:00","versionOfRecord":{"articleIdentity":"rs-8552663","link":"https://doi.org/10.1186/s12879-026-13107-x","journal":{"identity":"bmc-infectious-diseases","isVorOnly":false,"title":"BMC Infectious Diseases"},"publishedOn":"2026-03-20 15:59:47","publishedOnDateReadable":"March 20th, 2026"},"versionCreatedAt":"2026-01-16 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